To the best of our knowledge, previous studies in this regime mainly focus on comparing the NoVaS method with different GARCH-type models, and omit the potential of developing the NoVaS method itself further. Inspired by the development of ARCH model [1]} to GARCH model [2]}, this article attempts to build a novel NoVaS method which is derived from the GARCH(1,1) structure. Through extensive data analysis, we show that our methods bring significant improvement when the available data is short in length or is of more volatile nature. These are usually challenging forecasting exercise and thus our new method can open up some new directions.
